The present invention relates to product checkout devices and more specifically to a produce recognition system and method.
Bar code readers are well known for their usefulness in retail checkout and inventory control. Bar code readers are capable of identifying and recording most items during a typical transaction since most items are labeled with bar codes.
Items which are typically not identified and recorded by a bar code reader are produce items, since produce items are typically not labeled with bar codes. Bar code readers may include a scale for weighing produce items to assist in determining the price of such items. But identification of produce items is still a task for the checkout operator, who must identify a produce item and then manually enter an item identification code. Operator identification methods are slow and inefficient because they typically involve a visual comparison of a produce item with pictures of produce items, or a lookup of text in table. Operator identification methods are also prone to error, on the order of fifteen percent.
Therefore, it would be desirable to provide a produce recognition system and method. It would also be desirable to provide an accurate method of recognizing produce items.
In accordance with the teachings of the present invention, a produce recognition system and method are provided.
The system includes a produce data collector, a library, and a computer. The produce data collector collects first data from the produce item. The library contains second data associated with classes of produce items. The computer reads the second data from a library, determines a distance measure of likeness value between the first data and each of the second data, determines third data and a corresponding class of produce items from the second data which produces a smallest distance measure of likeness value, and identifies the produce item to be within the corresponding class of produce items.
A method of recognizing a produce item includes the steps of collecting first data from the produce item, reading a number of second data associated with a plurality of produce items including the one produce item from a library, determining a distance measure of likeness value between the first data and each of the second data, determining third data and a corresponding produce item from the second data which produces a smallest distance measure of likeness value, and identifying the produce item to be the corresponding produce item.
The identification step may optionally include the steps of ordering the distance measure of likeness values by size, displaying a list of the ordered distance measure of likeness values and corresponding names of produce items to an operator, and recording an operator choice for a produce item from the list.
The distance measure of likeness test for identifying produce items may be applied to other identification tasks in which sampled data is compared to reference data.
It is accordingly an object of the present invention to provide a produce recognition system and method.
It is another object of the present invention to provide a produce recognition system and method which identifies produce items by comparing their spectral data with the spectral data of items within classes of produce items.
It is another object of the present invention to define a distance measure of likeness (DML) value, which corresponds to the likelihood that an unknown instance is in a given class.
It is another object of the present invention to provide a produce recognition system and method which identifies produce items by sorting the distance measure of likeness (DML) values in ascending order and choosing the item with smallest distance as the most likely identification.